Points Cloud Classification using JointBoost Combined with Contextual Information for Feature Reduction

Abstract

Abstract: The requirements of 3D scene classification and understanding have dramatically increased with the widespread using of airborne LiDAR. This paper therefore focuses on complex power-line corridors scenes and presents an approach to automatically classify point clouds in building, ground, vegetation, power-line, and tower classes. Many key features of points cloud are introduced in this paper for classification using the JointBoost classifier. Due to the data of points cloud is “Big Data” and its classification rate is slow, we propose a method of serialized points cloud classification using spatial contextual information between objects for features reduction. The experiments prove that the classification method we study in this paper can be effectively used for points cloud classification in power-line corridors scenes.

Key words: LiDAR, point cloud classification, JointBoost, spatial context, feature reduction

CLC Number:

P237

References

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